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IEEE 9th International Conference on Advanced Computing (IACC)

作者： Verma, Akash Goyal, Shreya Thakur, Shridhar Kumar Gupta, Archit Gupta, Indrajeet Bhilai Inst Technol Dept CSE Durg India Natl Inst Technol Dept CSE Jalandhar Punjab India Galgotias Univ Dept Comp Sci Greater Noida India ABES Engn Coll Dept Comp Sci Ghaziabad India Bennett Univ Dept CSE Greater Noida India

ISBN: (纸本)9781728143927

In recent past, data mining, artificial intelligence, and machine learning have gained enormous attention to improve hospital performance. In some hospitals, medical personals want to improve their statists by decreasing the number of patients dying in the hospital. The research is focused on the mortality prediction of measurable outcomes, including the risk of complications & length of hospital stay. The duration spent in the hospital of the patient plays an important role both for patients & healthcare providers, influenced by numerous factors. LOS (length of stay) in critical care has great importance, both to the patient experience as well as the cost of care and is influenced by the complex environmental factors of the Hospitals. LOS is a parameter that is used to identify the extremity of illness & health-related resource utilization. This paper provides the improved prediction rate that a patient survives or dies in the range of length of stay in the hospital. It also anchors the analytical methods for the length of stay and mortality prediction.

关键词： machine learning algorithm Feature Scaling Feature Extraction Neural Networks Logistics Regression Support Vector machine

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A Computer-aided Diagnosis System for Glioma Grading using Three Dimensional Texture Analysis and machine learning in MRI Brain Tumour 3

A Computer-aided Diagnosis System for Glioma Grading using T...

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3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Al-Zurfi, Ahmed Naser Meziane, Farid Aspin, Rob Univ Salford Sch Comp Sci & Engn Salford Lancs England Univ Kufa Fac Engn Dept Commun Engn Kufa Iraq

ISBN: (纸本)9781728135786

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computer-aided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading.

关键词： Glioma Grading Grey level co-occurrence matrix Single and ensemble classification system machine learning algorithm

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Primary Node Selection algorithm of PBFT Based on Anomaly Detection and Reputation Model

Primary Node Selection Algorithm of PBFT Based on Anomaly De...

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作者： Ruowen Gu Bin Chen Dongyan Huang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology

The combination of reputation model and PBFT consensus algorithm is a competitive solution to improve consortium blockchains' TPS. However, there are still many problems for PBFT with reputation model such as detecting malicious nodes and centralization problem. In this paper, an anomaly detection algorithm base on for machine learning is adopted to detect the malicious nodes in consortium blockchains using PBFT consensus mechanism. And then, the results of anomaly detection are applied to evaluate the reputation value of the ***, random number is used to ensure the randomness and fairness of the PBFT primary node selection in the reputation model. The experimental test shows the anomaly detection model can improve significantly the consensus efficiency of PBFT. The test also shows that the use of random number can effectively avoid the centralization caused by the imbalance of the reputation value growth speed between nodes.

关键词： Consortium blockchains Reputation model PBFT Anomaly detection machine learning algorithm

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EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices

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DATA IN BRIEF 2024年 54卷 110261页

作者： Asante, Emmanuel Appiah, Obed Appiahene, Peter Adu, Kwabena Univ Energy & Nat Resources Dept Comp Sci & Informat Sunyani Ghana

Hyperspectral imaging, combined with deep learning techniques, has been employed to classify maize. However, the implementation of these automated methods often requires substantial processing and computing resources, presenting a significant challenge for deployment on embedded devices due to high GPU power consumption. Access to Ghanaian local maize data for such classification tasks is also extremely difficult in Ghana. To address these challenges, this research aims to create a simple dataset comprising three distinct types of local maize seeds in Ghana. The goal is to facilitate the development of an efficient maize classification tool that minimizes computational costs and reduces human involvement in the process of grading seeds for marketing and production. The dataset is presented in two parts: raw images, consisting of 4,846 images, are categorized into bad and good. Specifically, 2,211 images belong to the bad class, while 2,635 belong to the good class. Augmented images consist of 28,910 images, with 13,250 representing bad data and 15,660 representing good data. All images have been validated by experts from Heritage Seeds Ghana and are freely available for use within the research community. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： machine learning Maize dataset Image recognition machine learning algorithm Precision agriculture Convolutional Neural Network

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Implications of land use/land cover dynamics and Prosopis invasion on ecosystem service values in Afar Region, Ethiopia

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SCIENCE OF THE TOTAL ENVIRONMENT 2019年 675卷 354-366页

作者： Shiferaw, Hailu Bewket, Woldeamlak Alamirew, Tena Zeleke, Gete Teketay, Demel Bekele, Ketema Schaffner, Urs Eckert, Sandra Addis Ababa Univ Water & Land Resource Ctr POB 3880 Addis Ababa Ethiopia Addis Ababa Univ Dept Geog & Environm Studies PO 1176 Addis Ababa Ethiopia Botswana Univ Agr & Nat Resources BUAN Dept Crop Sci & Prod Private Bag 0027 Gaborone Botswana Haramaya Univ Sch Agr Econ & Agribusiness POB 50 Dire Dawa Ethiopia CABI Rue Grillons 1 CH-2800 Delemont Switzerland Univ Bern Ctr Dev & Environm Mittelstr 43 CH-3012 Bern Switzerland

Land use/land cover (LULC) dynamics and the resulting changes in ecosystems, as well as the services they provide, are a consequence of human activities and environmental drivers, such as invasive alien plant species. This study assessed the changes in LULC and ecosystem service values (ESVs) in the Afar National Regional State, Ethiopia, which experiences a rapid invasion by the alien tree Prosopis juliflora (Swartz DC). Landsat satellite data of 1986, 2000 and 2017 were used in Random Forest algorithm to assess LULC changes in the last 31 years, to calculate net changes for different LULC types and the associated changes in ESVs. Kappa accuracies of 88% and higher were obtained for the three LULC classifications. Post-classification change analyses for the period between 1986 and 2017 revealed a positive net change for Prosopis invaded areas, cropland, salt flats, settlements and waterbodies. The rate of Prosopis invasion was estimated at 31,127 ha per year. Negative net changes were found for grassland, bareland, bush-shrub-woodland, and natural forests. According to the local community representatives, the four most important drivers of LULC dynamics were climate change, frequent droughts, invasive species and weak traditional law. Based on two different ESVs estimations, the ecosystem changes caused by LULC changes resulted in an average loss of ESVs in the study area of about US$ 602 million (range US$ 112 to 1091 million) over the last 31 years. With an increase in area by 965,000 ha, Prosopis-invaded land was the highest net change during the study period, followed by grassland (-599,000 ha), bareland (-329,000 ha) and bush-shrub-woodland (-327,000 ha). Our study provides evidence that LULC changes in the Afar Region have led to a significant loss in ESVs, with serious consequences for the livelihoods of the rural people. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： Ecosystem service values Land use and cover dynamics machine learning algorithm Random forest classifier Prosopis juliflora Ethiopia

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Intelligent Safe Driving Methods Based on Hybrid Automata and Ensemble CART algorithms for Multihigh-Speed Trains

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第10期49卷 3816-3826页

作者： Cheng, Ruijun Yu, Wei Song, Yongduan Chen, Dewang Ma, Xiaoping Cheng, Yu Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Fujian Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chongqing Univ Sch Automat Chongqing 400044 Peoples R China China Acad Railway Sci Inst Infrastruct Inspect Res Beijing 100081 Peoples R China

Considering both the tracking safety of multi-HSTs and the operational efficiency of a single HST, intelligent safe driving methods (ISDMs) are proposed to obtain better speed-distance curves by integrating hybrid automata (HA) with data mining algorithms in this paper. To begin with, an intelligent safe distance controller is established by using HA to ensure the tracking safety of multi-HSTs' operation in real time. Then, data-driven intelligent driving methods based on ensemble algorithms (Bagging or Adaboost.R) and classification and regression tree (CART) are proposed to discover the potential driving rules from the field driving data. Furthermore, because of the continuous rise of HST's operation mileage, the driving data collected from HST has increased tremendously compared with the subways. So, an iterative pruning error minimization algorithm is designed to reduce the redundancy of the driving data and improve the computational speed of the learning process. Finally, compared with the automatic train operation (ATO) method, the energy consumption of B-CART, A-CART, and S-A-CART algorithms can be decreased by 3.32%, 3.80%, and 4.30%, respectively.

关键词： Automatic train operation (ATO) high-speed train intelligent train control machine learning algorithm sparse algorithm system safety

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A security risk plan search assistant decision algorithm using deep neural network combined with two-stage similarity calculation

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PERSONAL AND UBIQUITOUS COMPUTING 2019年第3-4期23卷 541-552页

作者： Hu, Jun Fang, Jun Du, Yanhua Liu, Zhe Ji, Pengyang Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan 430070 Hubei Peoples R China Zhengzhou Univ Aeronaut Sch Civil Engn & Architecture Zhengzhou 450046 Henan Peoples R China PowerChina Real Estate Grp Co Ltd Wuhan 430070 Hubei Peoples R China Wuhan Metro Grp Co Ltd Wuhan 430030 Hubei Peoples R China

In view of the nonlinearity and uncertainty of safety accident risk assessment, firstly, based on the deep neural network, the training criterion of the network is changed, and the triplet convolutional neural network with the similarity measure as the cost function is proposed. The inactive multi-scale set features are extracted from them, so that the semantic features obtained by learning are suitable for security risk image retrieval. In the image retrieval application, the training samples of the retrieved data set are not enough to train a large network, and the innovative application of migration learning to security risk image retrieval proposes to train the network with data sets similar to the retrieved data sets. Then based on the traditional nearest neighbor algorithm, this paper proposes a case similarity calculation method based on two-dimensional structure of structural similarity and attribute similarity, input the characteristic attribute value of the current emergency event, and conduct similar case retrieval. The final calculation returns the historical case and its solution that the user is most similar to the currently entered incident feature. The experiment proves that the maximum relative error between the output of the network and the expected output value is 5.17%, and the minimum relative error is 1.38%, which has high accuracy.

关键词： Full risk plan search Assisted decision making Deep neural network machine learning algorithm Case reasoning Image retrieval Two-stage similarity calculation

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Application of radiomics features selection and classification algorithms for medical imaging decision: MRI radiomics breast cancer cases study

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Informatics in Medicine Unlocked 2021年 27卷

作者： Laajili, Rihab Said, Mourad Tagina, Moncef COSMOS Laboratory National Institute of Computer Sciences Campus University of Mannouba Tunisia Radiology and Medical Imaging Department International Center Carthage Medical Monastir Tunisia

The accurate imaging classification of breast lesion is a capital step for diagnosis and appropriate treatment. The radiomics approach aims to extract and analyze quantitative multiple informative data from medical images unseen by the expert eyes, thus the need for enhancing the ability of the machine learning algorithm to classify different lesions. Generally the radiomics techniques have led to high dimensionality, which may cause overfitting, increasing the complexity of the predictive model and degrading the prediction performance. Thereby, feature selection aims at solving this problem by selecting the relevant features and removing the irrelevant and redundant ones. In this study we present the exploitation of heterogeneous radiomics features for supporting decision-based clinical tasks and the ability of various machine learning classifiers, combined with several feature selection methods, to accurately predict breast cancer nodules. This study uses 593 radiomics features to quantify the intensity, shape and texture of breast tumor images to classify the binary outcome of the benign or malignant status. The uncertainty feature selection, in addition to the decision tree have scores the best performance among 66 other feature selection/classifier combinations (accuracy = 0.85). On the other hand, the t-Test and the Gaussian naïve Bayes have the worst performing combination (accuracy = 0.49). This study proves also that morphological features influence the behavior of machine learning algorithms. © 2021

关键词： Breast cancer Classification Feature selection machine learning algorithm Medical decision making Radiomics

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Mapping and quantifying agricultural irrigation in heterogeneous landscapes using Google Earth Engine

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2021年 23卷

作者： Zurqani, H. A. Allen, J. S. Post, C. J. Pellett, C. A. Walker, T. C. Clemson Univ South Carolina Water Resources Ctr Clemson SC 29634 USA Univ Arkansas Agr Expt Stn Arkansas Forest Resources Ctr Monticello AR 71655 USA Univ Tripoli Dept Soil & Water Sci Tripoli 13538 Libya Clemson Univ Dept Forestry & Environm Conservat Clemson SC 29634 USA South Carolina Dept Nat Resources Clemson SC USA

Geospatial analysis using remote sensing data has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. Humid and heterogeneous landscapes such as those in the South Atlantic Coastal Plain are challenging for satellite image classification over large spatial extents. The objectives of this study are to: 1) compile relevant vegetation indices for mapping and quantifying irrigated and dryland agricultural areas across the coastal plain region of South Carolina, USA;2) identify the spatial and temporal change of the agricultural irrigation areas in the region over time;3) compare the results to county-level statistics reported by the United States Department of Agriculture (USDA) in the study area. In this study, a framework has been developed to regularly monitor irrigated agriculture's extent and distribution using high-spatial-resolution Sentinel-2 imagery for the coastal plain of South Carolina region using the Google Earth Engine (GEE) platform. The maps were produced using random forest supervised classification for three years with overall accuracy assessments of 83.73% (2016), 86.18% (2017), and 84.55% (2018). The major irrigated crops in the study area are corn, cotton, soybeans, and peanuts. Overall, the irrigated crops' distribution areas were mainly concentrated in counties near the center of the studied region. The produced maps will provide valuable information for the development of irrigated agriculture and the optimization of irrigation water use in the region. The methodology followed in this study can be applied to any other regions around the world with similar landscapes and climatic conditions.

关键词： Classification Irrigated crops Actual water use Remote sensing machine learning algorithm Random forest classifier Agriculture Water demand

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Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

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INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2019年第2期10卷 34-47页

作者： Lakshmi, Bagavathi Parthasarathy, S. Bharathiar Univ Res & Dev Ctr Coimbatore Tamil Nadu India Thiagarajar Coll Engn Dept Comp Applicat Madurai Tamil Nadu India

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

关键词： Convolution Neural Network Human Action Human Activity Recognition machine learning algorithm Max Pool Sensors

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